Versatile Vibrational Energy Sensors for Proteins

Abstract Vibrational energy transfer (VET) is emerging as key mechanism for protein functions, possibly playing an important role for energy dissipation, allosteric regulation, and enzyme catalysis. A deep understanding of VET is required to elucidate its role in such processes. Ultrafast VIS‐pump/IR‐probe spectroscopy can detect pathways of VET in proteins. However, the requirement of having a VET donor and a VET sensor installed simultaneously limits the possible target proteins and sites; to increase their number we compare six IR labels regarding their utility as VET sensors. We compare these labels in terms of their FTIR, and VET signature in VET donor‐sensor dipeptides in different solvents. Furthermore, we incorporated four of these labels in PDZ3 to assess their capabilities in more complex systems. Our results show that different IR labels can be used interchangeably, allowing for free choice of the right label depending on the system under investigation and the methods available.


Sample preparation
Dipeptides. Dipeptide samples for FTIR and VET measurements were prepared by dissolving 1 mg of the respective peptide in either 60 µl of tetrahydrofuran (THF) or dimethyl sulfoxide (DMSO). Aqueous samples were prepared in 20 µl 250 mM NaOH and diluted by addition of 40 µl H2O. The samples were mounted between two CaF2 windows separated by a 100 µm PTFE spacer, sealed with PTFE paste. The complete cell was moved by a Lissajous scanner within the focal plane during the measurement to continuously refresh the sample. Proteins. For the laser experiments, the protein was thawed on ice and concentrated to an appropriate concentration using centrifugal concentrators with a cut-off of 3 kDa. An AzAla-KQTSV peptide stock solution in sodium phosphate buffer pH 6.8 was prepared and the final concentration verified using UV/VIS spectroscopy and the absorption at 341 nm. The ligand was added to the protein in ~20% molar excess, resulting in final protein concentrations of 18 mM for I327SCN and ~8-11 mM for the other variants. The same procedure was followed to prepare a sample of wild-type PDZ (without the IR label) at a comparable concentration. The protein samples were mounted in a sample cell made of two CaF2 windows with a 100 µm PTFE spacer. This cell contained two sample chambers, separated by a barrier of PTFE and sealed with PTFE paste to prevent leakage from one compartment to the other (manuscript accepted [7] ). One of these compartments contained the PDZ mutant with the respective probe ncAA and the VET ligand, the other compartment contained the wild-type PDZ sample with the VET ligand. After each scan, the sample compartment was switched to enable quasi-simultaneous measurement of the background signal in the wild-type compartment. The complete cell was moved by a Lissajous scanner within the focal plane during the measurement to continuously refresh the sample.

UV/VIS and FTIR spectroscopy
Final dipeptide concentrations were determined using the AzAla absorption at 612 nm with an extinction coefficient of 328, 360 and 317 M -1 cm -1 in DMSO, THF, and H2O respectively. The extinction coefficients were determined from concentration series of the AzAla-Aha dipeptide. Protein concentrations were determined using the UV absorbance at 280 nm. All UV/VIS spectra were recorded with a Hitachi U-2000 spectrometer against the respective solvent background. FTIR spectra were recorded on a Bruker Tensor 27 FTIR spectrometer with 60 µm path length for aqueous and DMSO samples and 100 µm path length for samples in THF against the respective solvent background. The FTIR peaks of azide and nitrile vibrations were further isolated by fitting a polynomial to the region around the respective signal, omitting the peak itself.

Transient VIS-pump/IR-probe spectroscopy
A commercial amplified Ti:sapphire laser system (Mai Tai (oscillator), Empower (pump laser), Spitfire ace (amplifier) from Spectra Physics) was used to generate ~100 fs pulses of 800 nm light with a repetition rate of 3 kHz. The output of this laser system was split in two parts to operate two independent home-built optical parametric amplifiers (OPA). Both OPAs use a β-barium-borate (BBO) crystal in two amplification steps. In one OPA, difference frequency generation of signal and idler in a silver thiogallate (AgGaS2) crystal was used to generate probe and reference pulses around 2100 cm -1 . In the second OPA, the signal was frequency-doubled in another BBO to generate 612 nm pump pulses with 6 µJ pulse energy. The beams were focused into the sample (80 µm and 125 µm FWHM for IR and VIS beam, respectively). Pump and probe beam overlapped, the reference beam passed the sample roughly 2 mm above the other beams and was used to correct for fluctuations in the OPA output. The relative angle between the polarization of pump and probe beam was set to the magic angle for all experiments. IR pulses were dispersed on a 150 l/mm or 300 l/mm grating in a spectrometer (Triax 180, Jobin Yvon) and detected on a 2x64 pixel MCT array detector (Infrared Systems Development). A chopper wheel in the pump path allowed to measure unpumped/pumped spectra consecutively. The detector readout was linearized, referenced, difference spectra calculated and saved using home -written software (Python 3). A mechanical delay stage M-406.6PD (Physik Instrumente) was used to vary the delay between the pump and probe pulse, after recording 150000 (for shortest and longest delay time) or 30000 (all other delays) laser shots. All delays were scanned multiple times, and the data were averaged.

Data treatment
The difference signal at -40 ps delay time was subtracted as long-lived background from all other time points. The pump-probe data recorded in THF and DMSO were analyzed without further treatment. The samples in H2O show an intense background of solvent heating in the probed IR region. To correct for this thermal background, we scaled the difference spectrum of a late delay, i.e., after the decay of the VET-induced signal, to each earlier time point together with an offset parameter, assuming a constant band shape and subtracted it ( Figure S1). [8] Due to the large amplitude of the dipeptide signals, the region of the signal was omitted from this fit, leaving the pixels without azide or nitrile signal for the scaling of the H2O background signal. The data collected from both compartments of the PDZ sample cells were treated the same way. As the signals from the protein domain are much smaller than those of the dipeptides, we used the split sample cell to correct for minor artifacts, that could not be removed by the scaling of the solvent signal alone. To this end, the difference signal from the wild-type compartment was scaled to the difference signal of the mutant compartment at each delay and subtracted. The presented data are the average of two measurements per PDZ mutant. At each delay, the absolute values of pixels carrying the VET signal were integrated to yield the VET traces. A biexponential fit to the upper 35% of the traces was applied to determine the peak times. For the protein samples containing N3Phe, two distinct VET features can be observed. The traces and peak times reported here are those determined from the main feature. Due to the low signal-to-noise ratio of the side feature, its traces have not been analyzed in detail, but the dynamics appear to be very similar ( Figure S3). For the comparison of the signal sizes, the difference signals of the dipeptides were normalized to the peptide concentration s. The signals of the proteins were normalized to the protein concentration and to the difference signal of the solvent background at 2212 cm -1 , which represents a measure for the pump energy deposited in the probed sample region. Therefore, as other dependencies have been removed, the sizes of the VET signals in Figures 3 and 4 reflect the differences of the labels and effects of different environments.
To estimate the VET-induced shift of the azide and nitrile bands, we employed two approaches to fit the time-dependent IR difference spectra: (i) directly using the sample's FTIR spectrum as the band shape of both (negative and positive) signal components, and (ii) fitting a Voigt profile to the FTIR spectrum and use the determined parameters to model the negative feature and a Voigt profile with the same area but variable widths to account for the positive feature. The central wavenumber of the positive feature was shifted at each delay, while the position of the negative feature was kept constant. Due to the strong dependency between the signal amplitude and the shift, the amplitude of the negative component was fixed to 12% of the FTIR signal. This value was determined through calculations based on previous work of van Wilderen et al. [9] Briefly, the percentage of dipeptide excitation along the gaussian pump beam diameter was determined, under consideration of the local bea m intensity. The relative probe intensity along the beam diameter was determined and convoluted with the percentual e xcitation before integration, resulting in the percentage of molecules that were probed and previously excited within the probed volume.       Table S2. Figure S5. Comparison of the dynamics of the two features in the VET signal of PDZ N3Phe mutants. The general dynamics of both features appear similar. The signal-to-noise ratio of the side feature is significantly lower than that of main feature, due to the lower amplitude.  . Fit using the FTIR spectrum with an amplitude of -12% for the bleach and a shifted FTIR spectrum with an amplitude of +12% for the VET-induced absorption (dashed line) and fit using a Voigt band shape fitted to the FTIR spectrum with an amplitude of -12% modelling the bleach and a Voigt band shape with variable widths but the same area as the band representing the bleach (solid line). For clarity the spectra and fits have been scaled to the maximum of the TRIR data at each time point and offset. (right) Shift as determined for each time point by the fit with both different methods, shifted FTIR spectrum (blue) and shifted Voigt (red). For comparison with the dynamics determined from the TRIR data itself, the TRIR trace was scaled to the shift of the FTIR fit (gray). . Fit using the FTIR spectrum with an amplitude of -12% for the bleach and a shifted FTIR spectrum with an amplitude of +12% for the VET-induced absorption (dashed line) and fit using a Voigt band shape fitted to the FTIR spectrum with an amplitude of -12% modelling the bleach and a Voigt band shape with variable widths but the same area as the band representing the bleach (solid line). For clarity the spectra and fits have been scaled to the maximum of the TRIR data at each time point and offset. (right) Shift as determined for each time point by the fit with both different methods, shifted FTIR spectrum (blue) and shifted Voigt (red). For comparison with the dynamics determined from the TRIR data itself, the TRIR trace was scaled to the shift of the FTIR fit (gray). . Fit using the FTIR spectrum with an amplitude of -12% for the bleach and a shifted FTIR spectrum with an amplitude of +12% for the VET-induced absorption (dashed line) and fit using a Voigt band shape fitted to the FTIR spectrum with an amplitude of -12% modelling the bleach and a Voigt band shape with variable widths but the same area as the band representing the bleach (solid line). For clarity the spectra and fits have been scaled to the maximum of the TRIR data at each time point and offset. (right) Shift as determined for each time point by the fit with both different methods, shifted FTIR spectrum (blue) and shifted Voigt (red). For comparison with the dynamics determined from the TRIR data itself, the TRIR trace was scaled to the shift of the FTIR fit (gray).